def add_model_training_inputs(model):
"""Load the training dataset and attach the training inputs to the model."""
logger = logging.getLogger(__name__)
logger.info('Loading dataset: {}'.format(cfg.TRAIN.DATASETS))
roidb = combined_roidb_for_training(
cfg.TRAIN.DATASETS, cfg.TRAIN.PROPOSAL_FILES
)
logger.info('{:d} roidb entries'.format(len(roidb)))
model_builder.add_training_inputs(model, roidb=roidb)
def main(opts):
logger = logging.getLogger(__name__)
roidb = combined_roidb_for_training(
cfg.TRAIN.DATASETS, cfg.TRAIN.PROPOSAL_FILES)
logger.info('{:d} roidb entries'.format(len(roidb)))
roi_data_loader = RoIDataLoader(
roidb,
num_loaders=opts.num_loaders,
minibatch_queue_size=opts.minibatch_queue_size,
blobs_queue_capacity=opts.blobs_queue_capacity)
blob_names = roi_data_loader.get_output_names()
net = core.Net('dequeue_net')
net.type = 'dag'
all_blobs = []
for gpu_id in range(cfg.NUM_GPUS):
with core.NameScope('gpu_{}'.format(gpu_id)):
with core.DeviceScope(muji.OnGPU(gpu_id)):
for blob_name in blob_names:
blob = core.ScopedName(blob_name)
all_blobs.append(blob)
workspace.CreateBlob(blob)
logger.info('Creating blob: {}'.format(blob))
net.DequeueBlobs(
roi_data_loader._blobs_queue_name, blob_names)
logger.info("Protobuf:\n" + str(net.Proto()))
if opts.profiler:
import cProfile
cProfile.runctx(
'loader_loop(roi_data_loader)', globals(), locals(),
sort='cumulative')
else:
loader_loop(roi_data_loader)
roi_data_loader.register_sigint_handler()
roi_data_loader.start(prefill=True)
total_time = 0
for i in range(opts.num_batches):
start_t = time.time()
for _ in range(opts.x_factor):
workspace.RunNetOnce(net)
total_time += (time.time() - start_t) / opts.x_factor
logger.info('{:d}/{:d}: Averge dequeue time: {:.3f}s [{:d}/{:d}]'.
format(i + 1, opts.num_batches, total_time / (i + 1),
roi_data_loader._minibatch_queue.qsize(),
opts.minibatch_queue_size))
# Sleep to simulate the time taken by running a little network
time.sleep(opts.sleep_time)
# To inspect:
# blobs = workspace.FetchBlobs(all_blobs)
# from IPython import embed; embed()
logger.info('Shutting down data loader (EnqueueBlob errors are ok)...')
roi_data_loader.shutdown()
def main():
"""Main function"""
args = parse_args()
print('Called with args:')
print(args)
if not torch.cuda.is_available():
sys.exit("Need a CUDA device to run the code.")
if args.cuda or cfg.NUM_GPUS > 0:
cfg.CUDA = True
else:
raise ValueError("Need Cuda device to run !")
if args.dataset == "coco2017":
cfg.TRAIN.DATASETS = ('coco_2017_train',)
cfg.MODEL.NUM_CLASSES = 81
elif args.dataset == "keypoints_coco2017":
cfg.TRAIN.DATASETS = ('keypoints_coco_2017_train',)
cfg.MODEL.NUM_CLASSES = 2
else:
raise ValueError("Unexpected args.dataset: {}".format(args.dataset))
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
### Adaptively adjust some configs ###
original_batch_size = cfg.NUM_GPUS * cfg.TRAIN.IMS_PER_BATCH
if args.batch_size is None:
args.batch_size = original_batch_size
cfg.NUM_GPUS = torch.cuda.device_count()
assert (args.batch_size % cfg.NUM_GPUS) == 0, \
'batch_size: %d, NUM_GPUS: %d' % (args.batch_size, cfg.NUM_GPUS)
cfg.TRAIN.IMS_PER_BATCH = args.batch_size // cfg.NUM_GPUS
print('Batch size change from {} (in config file) to {}'.format(
original_batch_size, args.batch_size))
print('NUM_GPUs: %d, TRAIN.IMS_PER_BATCH: %d' % (cfg.NUM_GPUS, cfg.TRAIN.IMS_PER_BATCH))
if args.num_workers is not None:
cfg.DATA_LOADER.NUM_THREADS = args.num_workers
print('Number of data loading threads: %d' % cfg.DATA_LOADER.NUM_THREADS)
### Adjust learning based on batch size change linearly
old_base_lr = cfg.SOLVER.BASE_LR
cfg.SOLVER.BASE_LR *= args.batch_size / original_batch_size
print('Adjust BASE_LR linearly according to batch size change: {} --> {}'.format(
old_base_lr, cfg.SOLVER.BASE_LR))
### Overwrite some solver settings from command line arguments
if args.optimizer is not None:
cfg.SOLVER.TYPE = args.optimizer
if args.lr is not None:
cfg.SOLVER.BASE_LR = args.lr
if args.lr_decay_gamma is not None:
cfg.SOLVER.GAMMA = args.lr_decay_gamma
assert_and_infer_cfg()
timers = defaultdict(Timer)
### Dataset ###
timers['roidb'].tic()
roidb, ratio_list, ratio_index = combined_roidb_for_training(
cfg.TRAIN.DATASETS, cfg.TRAIN.PROPOSAL_FILES)
timers['roidb'].toc()
roidb_size = len(roidb)
logger.info('{:d} roidb entries'.format(roidb_size))
logger.info('Takes %.2f sec(s) to construct roidb', timers['roidb'].average_time)
# Effective training sample size for one epoch
train_size = roidb_size // args.batch_size * args.batch_size
sampler = MinibatchSampler(ratio_list, ratio_index)
dataset = RoiDataLoader(
roidb,
cfg.MODEL.NUM_CLASSES,
training=True)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=args.batch_size,
drop_last=True,
sampler=sampler,
num_workers=cfg.DATA_LOADER.NUM_THREADS,
collate_fn=collate_minibatch)
dataiterator = iter(dataloader)
### Model ###
maskRCNN = Generalized_RCNN()
if cfg.CUDA:
maskRCNN.cuda()
### Optimizer ###
bias_params = []
nonbias_params = []
for key, value in dict(maskRCNN.named_parameters()).items():
if value.requires_grad:
if 'bias' in key:
bias_params.append(value)
else:
nonbias_params.append(value)
# Learning rate of 0 is a dummy value to be set properly at the start of training
params = [
{'params': nonbias_params,
'lr': 0,
'weight_decay': cfg.SOLVER.WEIGHT_DECAY},
{'params': bias_params,
'lr': 0 * (cfg.SOLVER.BIAS_DOUBLE_LR + 1),
'weight_decay': cfg.SOLVER.WEIGHT_DECAY if cfg.SOLVER.BIAS_WEIGHT_DECAY else 0}
]
if cfg.SOLVER.TYPE == "SGD":
optimizer = torch.optim.SGD(params, momentum=cfg.SOLVER.MOMENTUM)
elif cfg.SOLVER.TYPE == "Adam":
optimizer = torch.optim.Adam(params)
### Load checkpoint
if args.load_ckpt:
load_name = args.load_ckpt
logging.info("loading checkpoint %s", load_name)
checkpoint = torch.load(load_name, map_location=lambda storage, loc: storage)
net_utils.load_ckpt(maskRCNN, checkpoint['model'])
if args.resume:
args.start_step = checkpoint['step'] + 1
if 'train_size' in checkpoint: # For backward compatibility
assert checkpoint['train_size'] == train_size
# There is a bug in optimizer.load_state_dict on Pytorch 0.3.1.
# However it's fixed on master.
# optimizer.load_state_dict(checkpoint['optimizer'])
misc_utils.load_optimizer_state_dict(optimizer, checkpoint['optimizer'])
del checkpoint
torch.cuda.empty_cache()
if args.load_detectron: #TODO resume for detectron weights (load sgd momentum values)
logging.info("loading Detectron weights %s", args.load_detectron)
load_detectron_weight(maskRCNN, args.load_detectron)
lr = optimizer.param_groups[0]['lr'] # lr of non-bias parameters, for commmand line outputs.
maskRCNN = mynn.DataParallel(maskRCNN, cpu_keywords=['im_info', 'roidb'],
minibatch=True)
### Training Setups ###
args.run_name = misc_utils.get_run_name() + '_step'
output_dir = misc_utils.get_output_dir(args, args.run_name)
args.cfg_filename = os.path.basename(args.cfg_file)
if not args.no_save:
if not os.path.exists(output_dir):
os.makedirs(output_dir)
blob = {'cfg': yaml.dump(cfg), 'args': args}
with open(os.path.join(output_dir, 'config_and_args.pkl'), 'wb') as f:
pickle.dump(blob, f, pickle.HIGHEST_PROTOCOL)
if args.use_tfboard:
from tensorboardX import SummaryWriter
# Set the Tensorboard logger
tblogger = SummaryWriter(output_dir)
### Training Loop ###
maskRCNN.train()
CHECKPOINT_PERIOD = int(cfg.TRAIN.SNAPSHOT_ITERS / cfg.NUM_GPUS)
# Set index for decay steps
decay_steps_ind = None
for i in range(1, len(cfg.SOLVER.STEPS)):
if cfg.SOLVER.STEPS[i] >= args.start_step:
decay_steps_ind = i
break
if decay_steps_ind is None:
decay_steps_ind = len(cfg.SOLVER.STEPS)
training_stats = TrainingStats(
args,
args.disp_interval,
tblogger if args.use_tfboard and not args.no_save else None)
try:
logger.info('Training starts !')
step = args.start_step
for step in range(args.start_step, cfg.SOLVER.MAX_ITER):
# Warm up
if step < cfg.SOLVER.WARM_UP_ITERS:
method = cfg.SOLVER.WARM_UP_METHOD
if method == 'constant':
warmup_factor = cfg.SOLVER.WARM_UP_FACTOR
elif method == 'linear':
alpha = step / cfg.SOLVER.WARM_UP_ITERS
warmup_factor = cfg.SOLVER.WARM_UP_FACTOR * (1 - alpha) + alpha
else:
raise KeyError('Unknown SOLVER.WARM_UP_METHOD: {}'.format(method))
lr_new = cfg.SOLVER.BASE_LR * warmup_factor
net_utils.update_learning_rate(optimizer, lr, lr_new)
lr = optimizer.param_groups[0]['lr']
assert lr == lr_new
elif step == cfg.SOLVER.WARM_UP_ITERS:
net_utils.update_learning_rate(optimizer, lr, cfg.SOLVER.BASE_LR)
lr = optimizer.param_groups[0]['lr']
assert lr == cfg.SOLVER.BASE_LR
# Learning rate decay
if decay_steps_ind < len(cfg.SOLVER.STEPS) and \
step == cfg.SOLVER.STEPS[decay_steps_ind]:
logger.info('Decay the learning on step %d', step)
lr_new = lr * cfg.SOLVER.GAMMA
net_utils.update_learning_rate(optimizer, lr, lr_new)
lr = optimizer.param_groups[0]['lr']
assert lr == lr_new
decay_steps_ind += 1
try:
input_data = next(dataiterator)
except StopIteration:
dataiterator = iter(dataloader)
input_data = next(dataiterator)
for key in input_data:
if key != 'roidb': # roidb is a list of ndarrays with inconsistent length
input_data[key] = list(map(Variable, input_data[key]))
training_stats.IterTic()
net_outputs = maskRCNN(**input_data)
training_stats.IterToc()
training_stats.UpdateIterStats(net_outputs)
training_stats.LogIterStats(step, lr)
loss = net_outputs['total_loss']
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (step+1) % CHECKPOINT_PERIOD == 0:
save_ckpt(output_dir, args, step, train_size, maskRCNN, optimizer)
# ---- Training ends ----
# Save last checkpoint
save_ckpt(output_dir, args, step, train_size, maskRCNN, optimizer)
except (RuntimeError, KeyboardInterrupt) as e:
has_exception = True
stack_trace = traceback.format_exc()
print(stack_trace)
finally:
if locals().get('has_exception'):
print('Save on exception')
save_ckpt(output_dir, args, step, train_size, maskRCNN, optimizer)
if args.use_tfboard and not args.no_save:
tblogger.close()
def main():
"""Main function"""
args = parse_args()
print('Called with args:')
print(args)
if not torch.cuda.is_available():
sys.exit("Need a CUDA device to run the code.")
if args.cuda or cfg.NUM_GPUS > 0:
cfg.CUDA = True
else:
raise ValueError("Need Cuda device to run !")
if args.dataset == "coco2017":
cfg.TRAIN.DATASETS = ('coco_2017_train',)
cfg.MODEL.NUM_CLASSES = 81
elif args.dataset == "keypoints_coco2017":
cfg.TRAIN.DATASETS = ('keypoints_coco_2017_train',)
cfg.MODEL.NUM_CLASSES = 2
else:
raise ValueError("Unexpected args.dataset: {}".format(args.dataset))
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
### Adaptively adjust some configs ###
original_batch_size = cfg.NUM_GPUS * cfg.TRAIN.IMS_PER_BATCH
original_ims_per_batch = cfg.TRAIN.IMS_PER_BATCH
original_num_gpus = cfg.NUM_GPUS
if args.batch_size is None:
args.batch_size = original_batch_size
cfg.NUM_GPUS = torch.cuda.device_count()
assert (args.batch_size % cfg.NUM_GPUS) == 0, \
'batch_size: %d, NUM_GPUS: %d' % (args.batch_size, cfg.NUM_GPUS)
cfg.TRAIN.IMS_PER_BATCH = args.batch_size // cfg.NUM_GPUS
effective_batch_size = args.iter_size * args.batch_size
print('effective_batch_size = batch_size * iter_size = %d * %d' % (args.batch_size, args.iter_size))
print('Adaptive config changes:')
print(' effective_batch_size: %d --> %d' % (original_batch_size, effective_batch_size))
print(' NUM_GPUS: %d --> %d' % (original_num_gpus, cfg.NUM_GPUS))
print(' IMS_PER_BATCH: %d --> %d' % (original_ims_per_batch, cfg.TRAIN.IMS_PER_BATCH))
### Adjust learning based on batch size change linearly
# For iter_size > 1, gradients are `accumulated`, so lr is scaled based
# on batch_size instead of effective_batch_size
old_base_lr = cfg.SOLVER.BASE_LR
cfg.SOLVER.BASE_LR *= args.batch_size / original_batch_size
print('Adjust BASE_LR linearly according to batch_size change:\n'
' BASE_LR: {} --> {}'.format(old_base_lr, cfg.SOLVER.BASE_LR))
### Adjust solver steps
step_scale = original_batch_size / effective_batch_size
old_solver_steps = cfg.SOLVER.STEPS
old_max_iter = cfg.SOLVER.MAX_ITER
cfg.SOLVER.STEPS = list(map(lambda x: int(x * step_scale + 0.5), cfg.SOLVER.STEPS))
cfg.SOLVER.MAX_ITER = int(cfg.SOLVER.MAX_ITER * step_scale + 0.5)
print('Adjust SOLVER.STEPS and SOLVER.MAX_ITER linearly based on effective_batch_size change:\n'
' SOLVER.STEPS: {} --> {}\n'
' SOLVER.MAX_ITER: {} --> {}'.format(old_solver_steps, cfg.SOLVER.STEPS,
old_max_iter, cfg.SOLVER.MAX_ITER))
# Scale FPN rpn_proposals collect size (post_nms_topN) in `collect` function
# of `collect_and_distribute_fpn_rpn_proposals.py`
#
# post_nms_topN = int(cfg[cfg_key].RPN_POST_NMS_TOP_N * cfg.FPN.RPN_COLLECT_SCALE + 0.5)
if cfg.FPN.FPN_ON and cfg.MODEL.FASTER_RCNN:
cfg.FPN.RPN_COLLECT_SCALE = cfg.TRAIN.IMS_PER_BATCH / original_ims_per_batch
print('Scale FPN rpn_proposals collect size directly propotional to the change of IMS_PER_BATCH:\n'
' cfg.FPN.RPN_COLLECT_SCALE: {}'.format(cfg.FPN.RPN_COLLECT_SCALE))
if args.num_workers is not None:
cfg.DATA_LOADER.NUM_THREADS = args.num_workers
print('Number of data loading threads: %d' % cfg.DATA_LOADER.NUM_THREADS)
### Overwrite some solver settings from command line arguments
if args.optimizer is not None:
cfg.SOLVER.TYPE = args.optimizer
if args.lr is not None:
cfg.SOLVER.BASE_LR = args.lr
if args.lr_decay_gamma is not None:
cfg.SOLVER.GAMMA = args.lr_decay_gamma
assert_and_infer_cfg()
timers = defaultdict(Timer)
### Dataset ###
timers['roidb'].tic()
roidb, ratio_list, ratio_index = combined_roidb_for_training(
cfg.TRAIN.DATASETS, cfg.TRAIN.PROPOSAL_FILES)
timers['roidb'].toc()
roidb_size = len(roidb)
logger.info('{:d} roidb entries'.format(roidb_size))
logger.info('Takes %.2f sec(s) to construct roidb', timers['roidb'].average_time)
# Effective training sample size for one epoch
train_size = roidb_size // args.batch_size * args.batch_size
batchSampler = BatchSampler(
sampler=MinibatchSampler(ratio_list, ratio_index),
batch_size=args.batch_size,
drop_last=True
)
dataset = RoiDataLoader(
roidb,
cfg.MODEL.NUM_CLASSES,
training=True)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_sampler=batchSampler,
num_workers=cfg.DATA_LOADER.NUM_THREADS,
collate_fn=collate_minibatch)
dataiterator = iter(dataloader)
### Model ###
maskRCNN = Generalized_RCNN()
if cfg.CUDA:
maskRCNN.cuda()
### Optimizer ###
gn_param_nameset = set()
for name, module in maskRCNN.named_modules():
if isinstance(module, nn.GroupNorm):
gn_param_nameset.add(name+'.weight')
gn_param_nameset.add(name+'.bias')
gn_params = []
gn_param_names = []
bias_params = []
bias_param_names = []
nonbias_params = []
nonbias_param_names = []
nograd_param_names = []
for key, value in dict(maskRCNN.named_parameters()).items():
if value.requires_grad:
if 'bias' in key:
bias_params.append(value)
bias_param_names.append(key)
elif key in gn_param_nameset:
gn_params.append(value)
gn_param_names.append(key)
else:
nonbias_params.append(value)
nonbias_param_names.append(key)
else:
nograd_param_names.append(key)
assert (gn_param_nameset - set(nograd_param_names) - set(bias_param_names)) == set(gn_param_names)
# Learning rate of 0 is a dummy value to be set properly at the start of training
params = [
{'params': nonbias_params,
'lr': 0,
'weight_decay': cfg.SOLVER.WEIGHT_DECAY},
{'params': bias_params,
'lr': 0 * (cfg.SOLVER.BIAS_DOUBLE_LR + 1),
'weight_decay': cfg.SOLVER.WEIGHT_DECAY if cfg.SOLVER.BIAS_WEIGHT_DECAY else 0},
{'params': gn_params,
'lr': 0,
'weight_decay': cfg.SOLVER.WEIGHT_DECAY_GN}
]
# names of paramerters for each paramter
param_names = [nonbias_param_names, bias_param_names, gn_param_names]
if cfg.SOLVER.TYPE == "SGD":
optimizer = torch.optim.SGD(params, momentum=cfg.SOLVER.MOMENTUM)
elif cfg.SOLVER.TYPE == "Adam":
optimizer = torch.optim.Adam(params)
### Load checkpoint
if args.load_ckpt:
load_name = args.load_ckpt
logging.info("loading checkpoint %s", load_name)
checkpoint = torch.load(load_name, map_location=lambda storage, loc: storage)
net_utils.load_ckpt(maskRCNN, checkpoint['model'])
if args.resume:
args.start_step = checkpoint['step'] + 1
if 'train_size' in checkpoint: # For backward compatibility
if checkpoint['train_size'] != train_size:
print('train_size value: %d different from the one in checkpoint: %d'
% (train_size, checkpoint['train_size']))
# reorder the params in optimizer checkpoint's params_groups if needed
# misc_utils.ensure_optimizer_ckpt_params_order(param_names, checkpoint)
# There is a bug in optimizer.load_state_dict on Pytorch 0.3.1.
# However it's fixed on master.
# optimizer.load_state_dict(checkpoint['optimizer'])
misc_utils.load_optimizer_state_dict(optimizer, checkpoint['optimizer'])
del checkpoint
torch.cuda.empty_cache()
if args.load_detectron: #TODO resume for detectron weights (load sgd momentum values)
logging.info("loading Detectron weights %s", args.load_detectron)
load_detectron_weight(maskRCNN, args.load_detectron)
lr = optimizer.param_groups[0]['lr'] # lr of non-bias parameters, for commmand line outputs.
maskRCNN = mynn.DataParallel(maskRCNN, cpu_keywords=['im_info', 'roidb'],
minibatch=True)
### Training Setups ###
args.run_name = misc_utils.get_run_name() + '_step'
output_dir = misc_utils.get_output_dir(args, args.run_name)
args.cfg_filename = os.path.basename(args.cfg_file)
if not args.no_save:
if not os.path.exists(output_dir):
os.makedirs(output_dir)
blob = {'cfg': yaml.dump(cfg), 'args': args}
with open(os.path.join(output_dir, 'config_and_args.pkl'), 'wb') as f:
pickle.dump(blob, f, pickle.HIGHEST_PROTOCOL)
if args.use_tfboard:
from tensorboardX import SummaryWriter
# Set the Tensorboard logger
tblogger = SummaryWriter(output_dir)
### Training Loop ###
maskRCNN.train()
CHECKPOINT_PERIOD = int(cfg.TRAIN.SNAPSHOT_ITERS / cfg.NUM_GPUS)
# Set index for decay steps
decay_steps_ind = None
for i in range(1, len(cfg.SOLVER.STEPS)):
if cfg.SOLVER.STEPS[i] >= args.start_step:
decay_steps_ind = i
break
if decay_steps_ind is None:
decay_steps_ind = len(cfg.SOLVER.STEPS)
training_stats = TrainingStats(
args,
args.disp_interval,
tblogger if args.use_tfboard and not args.no_save else None)
try:
logger.info('Training starts !')
step = args.start_step
for step in range(args.start_step, cfg.SOLVER.MAX_ITER):
# Warm up
if step < cfg.SOLVER.WARM_UP_ITERS:
method = cfg.SOLVER.WARM_UP_METHOD
if method == 'constant':
warmup_factor = cfg.SOLVER.WARM_UP_FACTOR
elif method == 'linear':
alpha = step / cfg.SOLVER.WARM_UP_ITERS
warmup_factor = cfg.SOLVER.WARM_UP_FACTOR * (1 - alpha) + alpha
else:
raise KeyError('Unknown SOLVER.WARM_UP_METHOD: {}'.format(method))
lr_new = cfg.SOLVER.BASE_LR * warmup_factor
net_utils.update_learning_rate(optimizer, lr, lr_new)
lr = optimizer.param_groups[0]['lr']
assert lr == lr_new
elif step == cfg.SOLVER.WARM_UP_ITERS:
net_utils.update_learning_rate(optimizer, lr, cfg.SOLVER.BASE_LR)
lr = optimizer.param_groups[0]['lr']
assert lr == cfg.SOLVER.BASE_LR
# Learning rate decay
if decay_steps_ind < len(cfg.SOLVER.STEPS) and \
step == cfg.SOLVER.STEPS[decay_steps_ind]:
logger.info('Decay the learning on step %d', step)
lr_new = lr * cfg.SOLVER.GAMMA
net_utils.update_learning_rate(optimizer, lr, lr_new)
lr = optimizer.param_groups[0]['lr']
assert lr == lr_new
decay_steps_ind += 1
training_stats.IterTic()
optimizer.zero_grad()
for inner_iter in range(args.iter_size):
try:
input_data = next(dataiterator)
except StopIteration:
dataiterator = iter(dataloader)
input_data = next(dataiterator)
for key in input_data:
if key != 'roidb': # roidb is a list of ndarrays with inconsistent length
input_data[key] = list(map(Variable, input_data[key]))
net_outputs = maskRCNN(**input_data)
training_stats.UpdateIterStats(net_outputs, inner_iter)
loss = net_outputs['total_loss']
loss.backward()
optimizer.step()
training_stats.IterToc()
training_stats.LogIterStats(step, lr)
if (step+1) % CHECKPOINT_PERIOD == 0:
save_ckpt(output_dir, args, step, train_size, maskRCNN, optimizer)
# ---- Training ends ----
# Save last checkpoint
save_ckpt(output_dir, args, step, train_size, maskRCNN, optimizer)
except (RuntimeError, KeyboardInterrupt):
del dataiterator
logger.info('Save ckpt on exception ...')
save_ckpt(output_dir, args, step, train_size, maskRCNN, optimizer)
logger.info('Save ckpt done.')
stack_trace = traceback.format_exc()
print(stack_trace)
finally:
if args.use_tfboard and not args.no_save:
tblogger.close()
def main():
"""Main function"""
args = parse_args()
print('Called with args:')
print(args)
if not torch.cuda.is_available():
sys.exit("Need a CUDA device to run the code.")
if args.cuda or cfg.NUM_GPUS > 0:
cfg.CUDA = True
else:
raise ValueError("Need Cuda device to run !")
if args.dataset == "custom_dataset" and args.num_classes is None:
raise ValueError(
"Need number of classes in your custom dataset to run!")
if args.dataset == "coco2017":
cfg.TRAIN.DATASETS = ('coco_2017_train', )
cfg.MODEL.NUM_CLASSES = 81
elif args.dataset == "keypoints_coco2017":
cfg.TRAIN.DATASETS = ('keypoints_coco_2017_train', )
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "voc2007":
cfg.TRAIN.DATASETS = ('voc_2007_train', )
cfg.MODEL.NUM_CLASSES = 21
elif args.dataset == "voc2012":
cfg.TRAIN.DATASETS = ('voc_2012_train', )
cfg.MODEL.NUM_CLASSES = 21
elif args.dataset == "custom_dataset":
cfg.TRAIN.DATASETS = ('custom_data_train', )
cfg.MODEL.NUM_CLASSES = args.num_classes
else:
raise ValueError("Unexpected args.dataset: {}".format(args.dataset))
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
### Adaptively adjust some configs ###
original_batch_size = cfg.NUM_GPUS * cfg.TRAIN.IMS_PER_BATCH
if args.batch_size is None:
args.batch_size = original_batch_size
cfg.NUM_GPUS = torch.cuda.device_count()
assert (args.batch_size % cfg.NUM_GPUS) == 0, \
'batch_size: %d, NUM_GPUS: %d' % (args.batch_size, cfg.NUM_GPUS)
cfg.TRAIN.IMS_PER_BATCH = args.batch_size // cfg.NUM_GPUS
print('Batch size change from {} (in config file) to {}'.format(
original_batch_size, args.batch_size))
print('NUM_GPUs: %d, TRAIN.IMS_PER_BATCH: %d' %
(cfg.NUM_GPUS, cfg.TRAIN.IMS_PER_BATCH))
if args.num_workers is not None:
cfg.DATA_LOADER.NUM_THREADS = args.num_workers
print('Number of data loading threads: %d' % cfg.DATA_LOADER.NUM_THREADS)
### Adjust learning based on batch size change linearly
old_base_lr = cfg.SOLVER.BASE_LR
cfg.SOLVER.BASE_LR *= args.batch_size / original_batch_size
print('Adjust BASE_LR linearly according to batch size change: {} --> {}'.
format(old_base_lr, cfg.SOLVER.BASE_LR))
### Overwrite some solver settings from command line arguments
if args.optimizer is not None:
cfg.SOLVER.TYPE = args.optimizer
if args.lr is not None:
cfg.SOLVER.BASE_LR = args.lr
if args.lr_decay_gamma is not None:
cfg.SOLVER.GAMMA = args.lr_decay_gamma
timers = defaultdict(Timer)
### Dataset ###
timers['roidb'].tic()
roidb, ratio_list, ratio_index = combined_roidb_for_training(
cfg.TRAIN.DATASETS, cfg.TRAIN.PROPOSAL_FILES)
timers['roidb'].toc()
train_size = len(roidb)
logger.info('{:d} roidb entries'.format(train_size))
logger.info('Takes %.2f sec(s) to construct roidb',
timers['roidb'].average_time)
sampler = MinibatchSampler(ratio_list, ratio_index)
dataset = RoiDataLoader(roidb, cfg.MODEL.NUM_CLASSES, training=True)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=args.batch_size,
sampler=sampler,
num_workers=cfg.DATA_LOADER.NUM_THREADS,
collate_fn=collate_minibatch)
assert_and_infer_cfg()
### Model ###
maskRCNN = Generalized_RCNN()
if cfg.CUDA:
maskRCNN.cuda()
### Optimizer ###
bias_params = []
nonbias_params = []
for key, value in dict(maskRCNN.named_parameters()).items():
if value.requires_grad:
if 'bias' in key:
bias_params.append(value)
else:
nonbias_params.append(value)
params = [{
'params': nonbias_params,
'lr': cfg.SOLVER.BASE_LR,
'weight_decay': cfg.SOLVER.WEIGHT_DECAY
}, {
'params':
bias_params,
'lr':
cfg.SOLVER.BASE_LR * (cfg.SOLVER.BIAS_DOUBLE_LR + 1),
'weight_decay':
cfg.SOLVER.WEIGHT_DECAY if cfg.SOLVER.BIAS_WEIGHT_DECAY else 0
}]
if cfg.SOLVER.TYPE == "SGD":
optimizer = torch.optim.SGD(params, momentum=cfg.SOLVER.MOMENTUM)
elif cfg.SOLVER.TYPE == "Adam":
optimizer = torch.optim.Adam(params)
### Load checkpoint
if args.load_ckpt:
load_name = args.load_ckpt
logging.info("loading checkpoint %s", load_name)
checkpoint = torch.load(load_name,
map_location=lambda storage, loc: storage)
net_utils.load_ckpt(maskRCNN, checkpoint['model'])
if args.resume:
assert checkpoint['iters_per_epoch'] == train_size // args.batch_size, \
"iters_per_epoch should match for resume"
# There is a bug in optimizer.load_state_dict on Pytorch 0.3.1.
# However it's fixed on master.
# optimizer.load_state_dict(checkpoint['optimizer'])
misc_utils.load_optimizer_state_dict(optimizer,
checkpoint['optimizer'])
if checkpoint['step'] == (checkpoint['iters_per_epoch'] - 1):
# Resume from end of an epoch
args.start_epoch = checkpoint['epoch'] + 1
args.start_iter = 0
else:
# Resume from the middle of an epoch.
# NOTE: dataloader is not synced with previous state
args.start_epoch = checkpoint['epoch']
args.start_iter = checkpoint['step'] + 1
del checkpoint
torch.cuda.empty_cache()
if args.load_detectron: #TODO resume for detectron weights (load sgd momentum values)
logging.info("loading Detectron weights %s", args.load_detectron)
load_detectron_weight(maskRCNN, args.load_detectron)
lr = optimizer.param_groups[0][
'lr'] # lr of non-bias parameters, for commmand line outputs.
maskRCNN = mynn.DataParallel(maskRCNN,
cpu_keywords=['im_info', 'roidb'],
minibatch=True)
### Training Setups ###
args.run_name = misc_utils.get_run_name()
output_dir = misc_utils.get_output_dir(args, args.run_name)
args.cfg_filename = os.path.basename(args.cfg_file)
if not args.no_save:
if not os.path.exists(output_dir):
os.makedirs(output_dir)
blob = {'cfg': yaml.dump(cfg), 'args': args}
with open(os.path.join(output_dir, 'config_and_args.pkl'), 'wb') as f:
pickle.dump(blob, f, pickle.HIGHEST_PROTOCOL)
if args.use_tfboard:
from tensorboardX import SummaryWriter
# Set the Tensorboard logger
tblogger = SummaryWriter(output_dir)
### Training Loop ###
maskRCNN.train()
training_stats = TrainingStats(
args, args.disp_interval,
tblogger if args.use_tfboard and not args.no_save else None)
iters_per_epoch = int(train_size / args.batch_size) # drop last
args.iters_per_epoch = iters_per_epoch
ckpt_interval_per_epoch = iters_per_epoch // args.ckpt_num_per_epoch
try:
logger.info('Training starts !')
args.step = args.start_iter
global_step = iters_per_epoch * args.start_epoch + args.step
for args.epoch in range(args.start_epoch,
args.start_epoch + args.num_epochs):
# ---- Start of epoch ----
# adjust learning rate
if args.lr_decay_epochs and args.epoch == args.lr_decay_epochs[
0] and args.start_iter == 0:
args.lr_decay_epochs.pop(0)
net_utils.decay_learning_rate(optimizer, lr, cfg.SOLVER.GAMMA)
lr *= cfg.SOLVER.GAMMA
for args.step, input_data in zip(
range(args.start_iter, iters_per_epoch), dataloader):
for key in input_data:
if key != 'roidb': # roidb is a list of ndarrays with inconsistent length
input_data[key] = list(map(Variable, input_data[key]))
training_stats.IterTic()
net_outputs = maskRCNN(**input_data)
training_stats.UpdateIterStats(net_outputs)
loss = net_outputs['total_loss']
optimizer.zero_grad()
loss.backward()
optimizer.step()
training_stats.IterToc()
if (args.step + 1) % ckpt_interval_per_epoch == 0:
net_utils.save_ckpt(output_dir, args, maskRCNN, optimizer)
if args.step % args.disp_interval == 0:
log_training_stats(training_stats, global_step, lr)
global_step += 1
# ---- End of epoch ----
# save checkpoint
net_utils.save_ckpt(output_dir, args, maskRCNN, optimizer)
# reset starting iter number after first epoch
args.start_iter = 0
# ---- Training ends ----
if iters_per_epoch % args.disp_interval != 0:
# log last stats at the end
log_training_stats(training_stats, global_step, lr)
except (RuntimeError, KeyboardInterrupt):
logger.info('Save ckpt on exception ...')
net_utils.save_ckpt(output_dir, args, maskRCNN, optimizer)
logger.info('Save ckpt done.')
stack_trace = traceback.format_exc()
print(stack_trace)
finally:
if args.use_tfboard and not args.no_save:
tblogger.close()
def main():
"""Main function"""
args = parse_args()
print('Called with args:')
print(args)
if not torch.cuda.is_available():
sys.exit("Need a CUDA device to run the code.")
if args.cuda or cfg.NUM_GPUS > 0:
cfg.CUDA = True
else:
raise ValueError("Need Cuda device to run !")
if args.dataset == "rsna2018_pos_neg_k1":
cfg.TRAIN.DATASETS = ('RSNA_2018_pos_train_k1','RSNA_2018_neg_train_k1', 'RSNA_2018_pos_train_k1', 'RSNA_2018_pos_train_k1')
cfg.TEST.DATASETS = ('RSNA_2018_pos_val_k1','RSNA_2018_neg_val_k1')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "rsna2018_pos_k1":
cfg.TRAIN.DATASETS = ('RSNA_2018_pos_train_k1', )
cfg.TEST.DATASETS = ('RSNA_2018_pos_val_k1',)
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "rsna2018_pos_neg_k2":
cfg.TRAIN.DATASETS = ('RSNA_2018_pos_train_k2','RSNA_2018_neg_train_k2', 'RSNA_2018_pos_train_k2', 'RSNA_2018_pos_train_k2')
cfg.TEST.DATASETS = ('RSNA_2018_pos_val_k2','RSNA_2018_neg_val_k2')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "rsna2018_pos_k2":
cfg.TRAIN.DATASETS = ('RSNA_2018_pos_train_k2', )
cfg.TEST.DATASETS = ('RSNA_2018_pos_val_k2',)
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "rsna2018_pos_neg_k3":
cfg.TRAIN.DATASETS = ('RSNA_2018_pos_train_k3','RSNA_2018_neg_train_k3', 'RSNA_2018_pos_train_k3', 'RSNA_2018_pos_train_k3')
cfg.TEST.DATASETS = ('RSNA_2018_pos_val_k3','RSNA_2018_neg_val_k3')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "rsna2018_pos_k3":
cfg.TRAIN.DATASETS = ('RSNA_2018_pos_train_k3', )
cfg.TEST.DATASETS = ('RSNA_2018_pos_val_k3',)
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "rsna2018_pos_neg":
cfg.TRAIN.DATASETS = ('RSNA_2018_pos_train','RSNA_2018_neg_train', 'RSNA_2018_pos_train', 'RSNA_2018_pos_train')
cfg.TEST.DATASETS = ('RSNA_2018_pos_val','RSNA_2018_neg_val')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "rsna2018_pos":
cfg.TRAIN.DATASETS = ('RSNA_2018_pos_train', )
cfg.TEST.DATASETS = ('RSNA_2018_pos_val',)
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "rsna2018_pos_neg_clahe":
cfg.TRAIN.DATASETS =('RSNA_2018_pos_train_clahe','RSNA_2018_neg_train_clahe',
'RSNA_2018_pos_train_clahe', 'RSNA_2018_pos_train_clahe')
cfg.TEST.DATASETS =('RSNA_2018_pos_val_clahe_clahe','RSNA_2018_neg_val_clahe')
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "rsna2018_pos_clahe":
cfg.TRAIN.DATASETS = ('RSNA_2018_pos_train_clahe', )
cfg.TEST.DATASETS = ('RSNA_2018_pos_val_clahe',)
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "keypoints_coco2017":
cfg.TRAIN.DATASETS = ('keypoints_coco_2017_train',)
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "coco2017":
cfg.TRAIN.DATASETS = ('coco_2017_val',)
cfg.TEST.DATASETS = ('coco_2017_val')
cfg.MODEL.NUM_CLASSES = 81
else:
raise ValueError("Unexpected args.dataset: {}".format(args.dataset))
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
### Adaptively adjust some configs ###
original_batch_size = cfg.NUM_GPUS * cfg.TRAIN.IMS_PER_BATCH
original_ims_per_batch = cfg.TRAIN.IMS_PER_BATCH
original_num_gpus = cfg.NUM_GPUS
if args.batch_size is None:
args.batch_size = original_batch_size
cfg.NUM_GPUS = torch.cuda.device_count()
assert (args.batch_size % cfg.NUM_GPUS) == 0, \
'batch_size: %d, NUM_GPUS: %d' % (args.batch_size, cfg.NUM_GPUS)
cfg.TRAIN.IMS_PER_BATCH = args.batch_size // cfg.NUM_GPUS
effective_batch_size = args.iter_size * args.batch_size
print('effective_batch_size = batch_size * iter_size = %d * %d' % (args.batch_size, args.iter_size))
print('Adaptive config changes:')
print(' effective_batch_size: %d --> %d' % (original_batch_size, effective_batch_size))
print(' NUM_GPUS: %d --> %d' % (original_num_gpus, cfg.NUM_GPUS))
print(' IMS_PER_BATCH: %d --> %d' % (original_ims_per_batch, cfg.TRAIN.IMS_PER_BATCH))
### Adjust learning based on batch size change linearly
# For iter_size > 1, gradients are `accumulated`, so lr is scaled based
# on batch_size instead of effective_batch_size
old_base_lr = cfg.SOLVER.BASE_LR
cfg.SOLVER.BASE_LR *= args.batch_size / original_batch_size
print('Adjust BASE_LR linearly according to batch_size change:\n'
' BASE_LR: {} --> {}'.format(old_base_lr, cfg.SOLVER.BASE_LR))
### Adjust solver steps
step_scale = original_batch_size / effective_batch_size
old_solver_steps = cfg.SOLVER.STEPS
old_max_iter = cfg.SOLVER.MAX_ITER
cfg.SOLVER.STEPS = list(map(lambda x: int(x * step_scale + 0.5), cfg.SOLVER.STEPS))
cfg.SOLVER.MAX_ITER = int(cfg.SOLVER.MAX_ITER * step_scale + 0.5)
print('Adjust SOLVER.STEPS and SOLVER.MAX_ITER linearly based on effective_batch_size change:\n'
' SOLVER.STEPS: {} --> {}\n'
' SOLVER.MAX_ITER: {} --> {}'.format(old_solver_steps, cfg.SOLVER.STEPS,
old_max_iter, cfg.SOLVER.MAX_ITER))
# Scale FPN rpn_proposals collect size (post_nms_topN) in `collect` function
# of `collect_and_distribute_fpn_rpn_proposals.py`
#
# post_nms_topN = int(cfg[cfg_key].RPN_POST_NMS_TOP_N * cfg.FPN.RPN_COLLECT_SCALE + 0.5)
if cfg.FPN.FPN_ON and cfg.MODEL.FASTER_RCNN:
cfg.FPN.RPN_COLLECT_SCALE = cfg.TRAIN.IMS_PER_BATCH / original_ims_per_batch
print('Scale FPN rpn_proposals collect size directly propotional to the change of IMS_PER_BATCH:\n'
' cfg.FPN.RPN_COLLECT_SCALE: {}'.format(cfg.FPN.RPN_COLLECT_SCALE))
if args.num_workers is not None:
cfg.DATA_LOADER.NUM_THREADS = args.num_workers
print('Number of data loading threads: %d' % cfg.DATA_LOADER.NUM_THREADS)
### Overwrite some solver settings from command line arguments
if args.optimizer is not None:
cfg.SOLVER.TYPE = args.optimizer
if args.lr is not None:
cfg.SOLVER.BASE_LR = args.lr
if args.lr_decay_gamma is not None:
cfg.SOLVER.GAMMA = args.lr_decay_gamma
assert_and_infer_cfg()
timers = defaultdict(Timer)
### Dataset ###
timers['roidb'].tic()
#print()
roidb, ratio_list, ratio_index = combined_roidb_for_training(
cfg.TRAIN.DATASETS, cfg.TRAIN.PROPOSAL_FILES)
timers['roidb'].toc()
roidb_size = len(roidb)
logger.info('{:d} roidb entries'.format(roidb_size))
logger.info('Takes %.2f sec(s) to construct roidb', timers['roidb'].average_time)
# Effective training sample size for one epoch
train_size = roidb_size // args.batch_size * args.batch_size
batchSampler = BatchSampler(
sampler=MinibatchSampler(ratio_list, ratio_index),
batch_size=args.batch_size,
drop_last=True
)
#batchSampler = BatchSampler(torch_sampler.Sampler, batch_size=args.batch_size, drop_last=True)
dataset = RoiDataLoader(
roidb,
cfg.MODEL.NUM_CLASSES,
training=True)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_sampler=batchSampler,
num_workers=cfg.DATA_LOADER.NUM_THREADS,
collate_fn=collate_minibatch)
dataiterator = iter(dataloader)
### Model ###
maskRCNN = Generalized_RCNN()
if cfg.CUDA:
maskRCNN.cuda()
### Optimizer ###
gn_param_nameset = set()
for name, module in maskRCNN.named_modules():
if isinstance(module, nn.GroupNorm):
gn_param_nameset.add(name+'.weight')
gn_param_nameset.add(name+'.bias')
gn_params = []
gn_param_names = []
bias_params = []
bias_param_names = []
nonbias_params = []
nonbias_param_names = []
nograd_param_names = []
for key, value in maskRCNN.named_parameters():
if value.requires_grad:
if 'bias' in key:
bias_params.append(value)
bias_param_names.append(key)
elif key in gn_param_nameset:
gn_params.append(value)
gn_param_names.append(key)
else:
nonbias_params.append(value)
nonbias_param_names.append(key)
else:
nograd_param_names.append(key)
assert (gn_param_nameset - set(nograd_param_names) - set(bias_param_names)) == set(gn_param_names)
# Learning rate of 0 is a dummy value to be set properly at the start of training
params = [
{'params': nonbias_params,
'lr': 0,
'weight_decay': cfg.SOLVER.WEIGHT_DECAY},
{'params': bias_params,
'lr': 0 * (cfg.SOLVER.BIAS_DOUBLE_LR + 1),
'weight_decay': cfg.SOLVER.WEIGHT_DECAY if cfg.SOLVER.BIAS_WEIGHT_DECAY else 0},
{'params': gn_params,
'lr': 0,
'weight_decay': cfg.SOLVER.WEIGHT_DECAY_GN}
]
# names of paramerters for each paramter
param_names = [nonbias_param_names, bias_param_names, gn_param_names]
if cfg.SOLVER.TYPE == "SGD":
optimizer = torch.optim.SGD(params, momentum=cfg.SOLVER.MOMENTUM)
elif cfg.SOLVER.TYPE == "Adam":
optimizer = torch.optim.Adam(params)
### Load checkpoint
if args.load_ckpt:
load_name = args.load_ckpt
logging.info("loading checkpoint %s", load_name)
checkpoint = torch.load(load_name, map_location=lambda storage, loc: storage)
net_utils.load_ckpt(maskRCNN, checkpoint['model'])
if args.resume:
args.start_step = checkpoint['step'] + 1
if 'train_size' in checkpoint: # For backward compatibility
if checkpoint['train_size'] != train_size:
print('train_size value: %d different from the one in checkpoint: %d'
% (train_size, checkpoint['train_size']))
# reorder the params in optimizer checkpoint's params_groups if needed
# misc_utils.ensure_optimizer_ckpt_params_order(param_names, checkpoint)
# There is a bug in optimizer.load_state_dict on Pytorch 0.3.1.
# However it's fixed on master.
optimizer.load_state_dict(checkpoint['optimizer'])
# misc_utils.load_optimizer_state_dict(optimizer, checkpoint['optimizer'])
del checkpoint
torch.cuda.empty_cache()
if args.load_detectron: #TODO resume for detectron weights (load sgd momentum values)
logging.info("loading Detectron weights %s", args.load_detectron)
load_detectron_weight(maskRCNN, args.load_detectron)
lr = optimizer.param_groups[0]['lr'] # lr of non-bias parameters, for commmand line outputs.
maskRCNN = mynn.DataParallel(maskRCNN, cpu_keywords=['im_info', 'roidb'],
minibatch=True)
### Training Setups ###
args.run_name = misc_utils.get_run_name() + '_step'
output_dir = misc_utils.get_output_dir(args, args.run_name)
args.cfg_filename = os.path.basename(args.cfg_file)
if not args.no_save:
if not os.path.exists(output_dir):
os.makedirs(output_dir)
blob = {'cfg': yaml.dump(cfg), 'args': args}
with open(os.path.join(output_dir, 'config_and_args.pkl'), 'wb') as f:
pickle.dump(blob, f, pickle.HIGHEST_PROTOCOL)
if args.use_tfboard:
from tensorboardX import SummaryWriter
# Set the Tensorboard logger
tblogger = SummaryWriter(output_dir)
### Training Loop ###
maskRCNN.train()
CHECKPOINT_PERIOD = int(cfg.TRAIN.SNAPSHOT_ITERS / cfg.NUM_GPUS)
# Set index for decay steps
decay_steps_ind = None
for i in range(1, len(cfg.SOLVER.STEPS)):
if cfg.SOLVER.STEPS[i] >= args.start_step:
decay_steps_ind = i
break
if decay_steps_ind is None:
decay_steps_ind = len(cfg.SOLVER.STEPS)
training_stats = TrainingStats(
args,
args.disp_interval,
tblogger if args.use_tfboard and not args.no_save else None)
best_score = 0
try:
logger.info('Training starts !')
step = args.start_step
for step in range(args.start_step, cfg.SOLVER.MAX_ITER):
# Warm up
if step < cfg.SOLVER.WARM_UP_ITERS:
method = cfg.SOLVER.WARM_UP_METHOD
if method == 'constant':
warmup_factor = cfg.SOLVER.WARM_UP_FACTOR
elif method == 'linear':
alpha = step / cfg.SOLVER.WARM_UP_ITERS
warmup_factor = cfg.SOLVER.WARM_UP_FACTOR * (1 - alpha) + alpha
else:
raise KeyError('Unknown SOLVER.WARM_UP_METHOD: {}'.format(method))
lr_new = cfg.SOLVER.BASE_LR * warmup_factor
net_utils.update_learning_rate(optimizer, lr, lr_new)
lr = optimizer.param_groups[0]['lr']
assert lr == lr_new
elif step == cfg.SOLVER.WARM_UP_ITERS:
net_utils.update_learning_rate(optimizer, lr, cfg.SOLVER.BASE_LR)
lr = optimizer.param_groups[0]['lr']
assert lr == cfg.SOLVER.BASE_LR
# Learning rate decay
if decay_steps_ind < len(cfg.SOLVER.STEPS) and \
step == cfg.SOLVER.STEPS[decay_steps_ind]:
logger.info('Decay the learning on step %d', step)
lr_new = lr * cfg.SOLVER.GAMMA
net_utils.update_learning_rate(optimizer, lr, lr_new)
lr = optimizer.param_groups[0]['lr']
assert lr == lr_new
decay_steps_ind += 1
training_stats.IterTic()
optimizer.zero_grad()
for inner_iter in range(args.iter_size):
try:
input_data = next(dataiterator)
except StopIteration:
dataiterator = iter(dataloader)
input_data = next(dataiterator)
for key in input_data:
if key != 'roidb': # roidb is a list of ndarrays with inconsistent length
input_data[key] = list(map(Variable, input_data[key]))
net_outputs = maskRCNN(**input_data)
training_stats.UpdateIterStats(net_outputs, inner_iter)
loss = net_outputs['total_loss']
loss.backward()
optimizer.step()
training_stats.IterToc()
training_stats.LogIterStats(step, lr)
if (step+1) % CHECKPOINT_PERIOD == 0:
perforamce = eval_net(model=maskRCNN, submit_threshold=0.5, dataset_names=cfg.TEST.DATASETS, output_dir=output_dir,
gt_file='/home1/data/Kaggle_RSNA_Pneumonia/annotations/k_folder_1/k_folder1_val.csv')
#gt_file='/home/wccui/RSNA/data/csv/val_pos_459_gt.csv')
#gt_file='/home/wccui/detectron_pytorch_RSNA_v10_9/data/csv/val_pos_459_gt.csv')
maskRCNN.train()
print("perforamce ",perforamce)
MAP, precision, recall, f1 = summary_per(perforamce)
is_best = MAP > best_score
prev_best_score = best_score
best_score = max(MAP, best_score)
if is_best:
print ('Best snapshot! {} -> {}'.format(prev_best_score, best_score))
save_best_ckpt(output_dir, args, step, train_size, maskRCNN, optimizer,best_score)
tblogger.add_scalar('val_MAP', MAP, step+1)
tblogger.add_scalar('val_precision', precision, step + 1)
tblogger.add_scalar('val_recall', recall, step + 1)
tblogger.add_scalar('val_f1', f1, step + 1)
save_ckpt(output_dir, args, step, train_size, maskRCNN, optimizer)
# ---- Training ends ----
# Save last checkpoint
save_ckpt(output_dir, args, step, train_size, maskRCNN, optimizer)
except (RuntimeError, KeyboardInterrupt):
del dataiterator
logger.info('Save ckpt on exception ...')
save_ckpt(output_dir, args, step, train_size, maskRCNN, optimizer)
logger.info('Save ckpt done.')
stack_trace = traceback.format_exc()
print(stack_trace)
finally:
if args.use_tfboard and not args.no_save:
tblogger.close()
def main():
netStructure = SummaryWriter(
log_dir=
'/home/zhanwj/Desktop/pyTorch/Detectron.pytorch/network_structure/dispSeg/'
)
saveNetStructure = False
"""Main function"""
args = parse_args()
print('Called with args:')
print(args)
if not torch.cuda.is_available():
sys.exit("Need a CUDA device to run the code.")
if args.cuda or cfg.NUM_GPUS > 0:
#set gpu device
os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(
[str(ids) for ids in args.device_ids])
torch.backends.cudnn.benchmark = True
cfg.CUDA = True
else:
raise ValueError("Need Cuda device to run !")
if args.dataset == "coco2017":
cfg.TRAIN.DATASETS = ('coco_2017_train', )
cfg.MODEL.NUM_CLASSES = 81
elif args.dataset == "keypoints_coco2017":
cfg.TRAIN.DATASETS = ('keypoints_coco_2017_train', )
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "cityscapes":
cfg.TRAIN.DATASETS = ('cityscapes_semseg_train', )
cfg.MODEL.NUM_CLASSES = 19
else:
raise ValueError("Unexpected args.dataset: {}".format(args.dataset))
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
### Adaptively adjust some configs ###
original_batch_size = cfg.NUM_GPUS * cfg.TRAIN.IMS_PER_BATCH
if args.batch_size is None:
args.batch_size = original_batch_size
cfg.NUM_GPUS = torch.cuda.device_count()
assert (args.batch_size % cfg.NUM_GPUS) == 0, \
'batch_size: %d, NUM_GPUS: %d' % (args.batch_size, cfg.NUM_GPUS)
cfg.TRAIN.IMS_PER_BATCH = args.batch_size // cfg.NUM_GPUS
print('Batch size change from {} (in config file) to {}'.format(
original_batch_size, args.batch_size))
print('NUM_GPUs: %d, TRAIN.IMS_PER_BATCH: %d' %
(cfg.NUM_GPUS, cfg.TRAIN.IMS_PER_BATCH))
if args.num_workers is not None:
cfg.DATA_LOADER.NUM_THREADS = args.num_workers
print('Number of data loading threads: %d' % cfg.DATA_LOADER.NUM_THREADS)
### Adjust learning based on batch size change linearly
old_base_lr = cfg.SOLVER.BASE_LR
cfg.SOLVER.BASE_LR *= args.batch_size / original_batch_size
print('Adjust BASE_LR linearly according to batch size change: {} --> {}'.
format(old_base_lr, cfg.SOLVER.BASE_LR))
### Overwrite some solver settings from command line arguments
if args.optimizer is not None:
cfg.SOLVER.TYPE = args.optimizer
if args.lr is not None:
cfg.SOLVER.BASE_LR = args.lr
if args.lr_decay_gamma is not None:
cfg.SOLVER.GAMMA = args.lr_decay_gamma
timers = defaultdict(Timer)
### Dataset ###
timers['roidb'].tic()
if cfg.SEM.SEM_ON or cfg.DISP.DISP_ON:
roidb, ratio_list, ratio_index = combined_roidb_for_training_semseg(
cfg.TRAIN.DATASETS)
else:
roidb, ratio_list, ratio_index = combined_roidb_for_training(
cfg.TRAIN.DATASETS, cfg.TRAIN.PROPOSAL_FILES)
timers['roidb'].toc()
train_size = len(roidb)
logger.info('{:d} roidb entries'.format(train_size))
logger.info('Takes %.2f sec(s) to construct roidb',
timers['roidb'].average_time)
sampler = MinibatchSampler(ratio_list, ratio_index)
dataset = RoiDataLoader(roidb, cfg.MODEL.NUM_CLASSES, training=True)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=args.batch_size,
sampler=sampler,
num_workers=cfg.DATA_LOADER.NUM_THREADS,
collate_fn=collate_minibatch_semseg
if cfg.SEM.SEM_ON or cfg.DISP.DISP_ON else collate_minibatch)
assert_and_infer_cfg()
#for args.step, input_data in zip(range(100), dataloader):
# data_L = input_data['data']
# data_R = input_data['data_R']
# label = input_data['disp_label_0']
# cv2.imwrite('ims_L.png', data_L[0].numpy()[0].transpose(1,2,0)[:,:,::-1]+cfg.PIXEL_MEANS)
# cv2.imwrite('ims_R.png', data_R[0].numpy()[0].transpose(1,2,0)[:,:,::-1]+cfg.PIXEL_MEANS)
# cv2.imwrite('label.png', label[0].numpy()[0])
# return
### Model ###
maskRCNN = eval(cfg.MODEL.TYPE)()
"""
if cfg.SEM.SEM_ON:
if not cfg.DISP.DISP_ON:
maskRCNN = Generalized_SEMSEG()
else:
maskRCNN = Generalized_3DSD()
else:
maskRCNN = Generalized_RCNN()
"""
if cfg.CUDA:
maskRCNN.to('cuda')
#print(maskRCNN)
### Optimizer ###
bias_params = []
nonbias_params = []
for key, value in dict(maskRCNN.named_parameters()).items():
if value.requires_grad:
if 'bias' in key:
bias_params.append(value)
else:
nonbias_params.append(value)
params = [{
'params': nonbias_params,
'lr': cfg.SOLVER.BASE_LR,
'weight_decay': cfg.SOLVER.WEIGHT_DECAY
}, {
'params':
bias_params,
'lr':
cfg.SOLVER.BASE_LR * (cfg.SOLVER.BIAS_DOUBLE_LR + 1),
'weight_decay':
cfg.SOLVER.WEIGHT_DECAY if cfg.SOLVER.BIAS_WEIGHT_DECAY else 0
}]
if cfg.SOLVER.TYPE == "SGD":
optimizer = torch.optim.SGD(params, momentum=cfg.SOLVER.MOMENTUM)
elif cfg.SOLVER.TYPE == "Adam":
optimizer = torch.optim.Adam(params)
### Load checkpoint
if args.load_ckpt:
load_name = args.load_ckpt
logging.info("loading checkpoint %s", load_name)
checkpoint = torch.load(load_name,
map_location=lambda storage, loc: storage)
net_utils.load_ckpt(maskRCNN, checkpoint['model'])
if args.resume:
assert checkpoint['iters_per_epoch'] == train_size // args.batch_size, \
"iters_per_epoch should match for resume"
# There is a bug in optimizer.load_state_dict on Pytorch 0.3.1.
# However it's fixed on master.
# optimizer.load_state_dict(checkpoint['optimizer'])
misc_utils.load_optimizer_state_dict(optimizer,
checkpoint['optimizer'])
if checkpoint['step'] == (checkpoint['iters_per_epoch'] - 1):
# Resume from end of an epoch
args.start_epoch = checkpoint['epoch'] + 1
args.start_iter = 0
else:
# Resume from the middle of an epoch.
# NOTE: dataloader is not synced with previous state
args.start_epoch = checkpoint['epoch']
args.start_iter = checkpoint['step'] + 1
del checkpoint
torch.cuda.empty_cache()
if args.load_detectron: #TODO resume for detectron weights (load sgd momentum values)
logging.info("loading Detectron weights %s", args.load_detectron)
load_detectron_weight(maskRCNN, args.load_detectron)
lr = optimizer.param_groups[0][
'lr'] # lr of non-bias parameters, for commmand line outputs.
maskRCNN = mynn.DataParallel(maskRCNN,
cpu_keywords=['im_info', 'roidb'],
minibatch=True)
### Training Setups ###
args.run_name = misc_utils.get_run_name()
output_dir = misc_utils.get_output_dir(args, args.run_name)
args.cfg_filename = os.path.basename(args.cfg_file)
if not args.no_save:
if not os.path.exists(output_dir):
os.makedirs(output_dir)
blob = {'cfg': yaml.dump(cfg), 'args': args}
with open(os.path.join(output_dir, 'config_and_args.pkl'), 'wb') as f:
pickle.dump(blob, f, pickle.HIGHEST_PROTOCOL)
if args.use_tfboard:
#from tensorboardX import SummaryWriter
# Set the Tensorboard logger
tblogger = SummaryWriter(output_dir)
### Training Loop ###
maskRCNN.train()
training_stats = TrainingStats(
args, args.disp_interval,
tblogger if args.use_tfboard and not args.no_save else None)
iters_per_epoch = int(train_size / args.batch_size) # drop last
args.iters_per_epoch = iters_per_epoch
ckpt_interval_per_epoch = iters_per_epoch // args.ckpt_num_per_epoch
try:
logger.info('Training starts !')
args.step = args.start_iter
global_step = iters_per_epoch * args.start_epoch + args.step
for args.epoch in range(args.start_epoch,
args.start_epoch + args.num_epochs):
# ---- Start of epoch ----
# adjust learning rate
if args.lr_decay_epochs and args.epoch == args.lr_decay_epochs[
0] and args.start_iter == 0:
args.lr_decay_epochs.pop(0)
net_utils.decay_learning_rate(optimizer, lr, cfg.SOLVER.GAMMA)
lr *= cfg.SOLVER.GAMMA
for args.step, input_data in zip(
range(args.start_iter, iters_per_epoch), dataloader):
if cfg.DISP.DISP_ON:
input_data['data'] = list(
map(lambda x, y: torch.cat((x, y), dim=0),
input_data['data'], input_data['data_R']))
if cfg.SEM.DECODER_TYPE.endswith('3D'):
input_data['disp_scans'] = torch.arange(
1, cfg.DISP.MAX_DISPLACEMENT + 1).float().view(
1, cfg.DISP.MAX_DISPLACEMENT).repeat(
args.batch_size, 1)
del input_data['data_R']
for key in input_data:
if key != 'roidb': # roidb is a list of ndarrays with inconsistent length
input_data[key] = list(
map(
lambda x: Variable(x, requires_grad=False).to(
'cuda'), input_data[key]))
training_stats.IterTic()
net_outputs = maskRCNN(**input_data)
training_stats.UpdateIterStats(net_outputs)
#loss = net_outputs['losses']['loss_semseg']
#acc = net_outputs['metrics']['accuracy_pixel']
#print (loss.item(), acc)
#for key in net_outputs.keys():
# print(key)
loss = net_outputs['total_loss']
#print("loss.shape:",loss)
optimizer.zero_grad()
loss.backward()
optimizer.step()
training_stats.IterToc()
if args.step % args.disp_interval == 0:
disp_image = net_outputs['disp_image']
#tblogger.add_image('disp_image',disp_image,global_step)
#tblogger.add_image('semseg_image',semseg_image,global_step)
log_training_stats(training_stats, global_step, lr)
global_step += 1
# ---- End of epoch ----
# save checkpoint
net_utils.save_ckpt(output_dir, args, maskRCNN, optimizer)
# reset starting iter number after first epoch
args.start_iter = 0
# ---- Training ends ----
#if iters_per_epoch % args.disp_interval != 0:
# log last stats at the end
# log_training_stats(training_stats, global_step, lr)
except (RuntimeError, KeyboardInterrupt):
logger.info('Save ckpt on exception ...')
net_utils.save_ckpt(output_dir, args, maskRCNN, optimizer)
logger.info('Save ckpt done.')
stack_trace = traceback.format_exc()
print(stack_trace)
finally:
if args.use_tfboard and not args.no_save:
tblogger.close()
def main():
"""Main function"""
args = parse_args()
print('Called with args:')
print(args)
if not torch.cuda.is_available():
sys.exit("Need a CUDA device to run the code.")
if args.cuda or cfg.NUM_GPUS > 0:
cfg.CUDA = True
else:
raise ValueError("Need Cuda device to run !")
merge_cfg_from_file(args.cfg_file)
# Some manual adjustment for the ApolloScape dataset parameters here
cfg.OUTPUT_DIR = args.output_dir
cfg.TRAIN.DATASETS = 'TLESS'
cfg.MODEL.NUM_CLASSES = 30
# We have only one class for all CAD models
cfg.MODEL.NUMBER_CARS = 1
cfg.TRAIN.MIN_AREA = 196 # 14*14
cfg.TRAIN.USE_FLIPPED = False # Currently I don't know how to handle the flipped case
cfg.TRAIN.IMS_PER_BATCH = 1
cfg.NUM_GPUS = torch.cuda.device_count()
effective_batch_size = cfg.TRAIN.IMS_PER_BATCH * cfg.NUM_GPUS * args.iter_size
### Adaptively adjust some configs ###
original_batch_size = cfg.NUM_GPUS * cfg.TRAIN.IMS_PER_BATCH
original_ims_per_batch = cfg.TRAIN.IMS_PER_BATCH
original_num_gpus = cfg.NUM_GPUS
if args.batch_size is None:
args.batch_size = original_batch_size
assert (args.batch_size %
cfg.NUM_GPUS) == 0, 'batch_size: %d, NUM_GPUS: %d' % (
args.batch_size, cfg.NUM_GPUS)
print('effective_batch_size = batch_size * iter_size = %d * %d' %
(args.batch_size, args.iter_size))
print('Adaptive config changes:')
print(' effective_batch_size: %d --> %d' %
(original_batch_size, effective_batch_size))
print(' NUM_GPUS: %d --> %d' %
(original_num_gpus, cfg.NUM_GPUS))
print(' IMS_PER_BATCH: %d --> %d' %
(original_ims_per_batch, cfg.TRAIN.IMS_PER_BATCH))
### Adjust learning based on batch size change linearly
# For iter_size > 1, gradients are `accumulated`, so lr is scaled based
# on batch_size instead of effective_batch_size
old_base_lr = cfg.SOLVER.BASE_LR
cfg.SOLVER.BASE_LR *= args.batch_size / original_batch_size
print(
'Adjust BASE_LR linearly according to batch_size change:\n BASE_LR: {} --> {}'
.format(old_base_lr, cfg.SOLVER.BASE_LR))
### Adjust solver steps
step_scale = original_batch_size / effective_batch_size
old_solver_steps = cfg.SOLVER.STEPS
old_max_iter = cfg.SOLVER.MAX_ITER
cfg.SOLVER.STEPS = list(
map(lambda x: int(x * step_scale + 0.5), cfg.SOLVER.STEPS))
cfg.SOLVER.MAX_ITER = int(cfg.SOLVER.MAX_ITER * step_scale + 0.5)
print(
'Adjust SOLVER.STEPS and SOLVER.MAX_ITER linearly based on effective_batch_size change:\n'
' SOLVER.STEPS: {} --> {}\n'
' SOLVER.MAX_ITER: {} --> {}'.format(old_solver_steps,
cfg.SOLVER.STEPS, old_max_iter,
cfg.SOLVER.MAX_ITER))
# Scale FPN rpn_proposals collect size (post_nms_topN) in `collect` function
# of `collect_and_distribute_fpn_rpn_proposals.py`
#
# post_nms_topN = int(cfg[cfg_key].RPN_POST_NMS_TOP_N * cfg.FPN.RPN_COLLECT_SCALE + 0.5)
if cfg.FPN.FPN_ON and cfg.MODEL.FASTER_RCNN:
cfg.FPN.RPN_COLLECT_SCALE = cfg.TRAIN.IMS_PER_BATCH / original_ims_per_batch
print(
'Scale FPN rpn_proposals collect size directly propotional to the change of IMS_PER_BATCH:\n'
' cfg.FPN.RPN_COLLECT_SCALE: {}'.format(
cfg.FPN.RPN_COLLECT_SCALE))
if args.num_workers is not None:
cfg.DATA_LOADER.NUM_THREADS = args.num_workers
print('Number of data loading threads: %d' % cfg.DATA_LOADER.NUM_THREADS)
### Overwrite some solver settings from command line arguments
if args.optimizer is not None:
cfg.SOLVER.TYPE = args.optimizer
if args.lr is not None:
cfg.SOLVER.BASE_LR = args.lr
if args.lr_decay_gamma is not None:
cfg.SOLVER.GAMMA = args.lr_decay_gamma
assert_and_infer_cfg()
timers = defaultdict(Timer)
### Dataset ###
timers['roidb'].tic()
if cfg.MODEL.LOSS_3D_2D_ON:
roidb, ratio_list, ratio_index, ds = combined_roidb_for_training(
cfg.TRAIN.DATASETS, args.dataset_dir)
else:
roidb, ratio_list, ratio_index = combined_roidb_for_training(
cfg.TRAIN.DATASETS, args.dataset_dir)
timers['roidb'].toc()
roidb_size = len(roidb)
logger.info('{:d} roidb entries'.format(roidb_size))
logger.info('Takes %.2f sec(s) to construct roidb',
timers['roidb'].average_time)
# Effective training sample size for one epoch
train_size = roidb_size // args.batch_size * args.batch_size
sampler = MinibatchSampler(ratio_list, ratio_index)
dataset = RoiDataLoader(roidb,
cfg.MODEL.NUM_CLASSES,
training=True,
valid_keys=[
'has_visible_keypoints', 'boxes', 'seg_areas',
'gt_classes', 'gt_overlaps',
'box_to_gt_ind_map', 'segms', 'is_crowd',
'car_cat_classes', 'poses', 'quaternions',
'im_info'
])
dataloader = torch.utils.data.DataLoader(
dataset,
batch_size=args.batch_size,
drop_last=True,
sampler=sampler,
num_workers=cfg.DATA_LOADER.NUM_THREADS,
collate_fn=collate_minibatch)
dataiterator = iter(dataloader)
### Model ###
if cfg.MODEL.LOSS_3D_2D_ON:
maskRCNN = Generalized_RCNN(ds.Car3D)
else:
maskRCNN = Generalized_RCNN()
if cfg.CUDA:
maskRCNN.cuda()
### Optimizer ###
bias_params = []
bias_param_names = []
nonbias_params = []
nonbias_param_names = []
for key, value in dict(maskRCNN.named_parameters()).items():
if value.requires_grad:
if 'bias' in key:
bias_params.append(value)
bias_param_names.append(key)
else:
nonbias_params.append(value)
nonbias_param_names.append(key)
# Learning rate of 0 is a dummy value to be set properly at the start of training
params = [{
'params': nonbias_params,
'lr': 0,
'weight_decay': cfg.SOLVER.WEIGHT_DECAY
}, {
'params':
bias_params,
'lr':
0 * (cfg.SOLVER.BIAS_DOUBLE_LR + 1),
'weight_decay':
cfg.SOLVER.WEIGHT_DECAY if cfg.SOLVER.BIAS_WEIGHT_DECAY else 0
}]
if cfg.SOLVER.TYPE == "SGD":
optimizer = torch.optim.SGD(params, momentum=cfg.SOLVER.MOMENTUM)
elif cfg.SOLVER.TYPE == "Adam":
optimizer = torch.optim.Adam(params)
### Load checkpoint
if args.load_ckpt:
load_name = args.load_ckpt
logging.info("loading checkpoint %s", load_name)
checkpoint = torch.load(load_name,
map_location=lambda storage, loc: storage)
net_utils.load_ckpt(maskRCNN,
checkpoint['model'],
ignore_list=args.ckpt_ignore_head)
if args.resume:
args.start_step = checkpoint['step'] + 1
if 'train_size' in checkpoint: # For backward compatibility
if checkpoint['train_size'] != train_size:
print(
'train_size value: %d different from the one in checkpoint: %d'
% (train_size, checkpoint['train_size']))
# reorder the params in optimizer checkpoint's params_groups if needed
# misc_utils.ensure_optimizer_ckpt_params_order(param_names, checkpoint)
# There is a bug in optimizer.load_state_dict on Pytorch 0.3.1.
# However it's fixed on master.
# optimizer.load_state_dict(checkpoint['optimizer'])
misc_utils.load_optimizer_state_dict(optimizer,
checkpoint['optimizer'])
del checkpoint
torch.cuda.empty_cache()
if args.load_detectron: # TODO resume for detectron weights (load sgd momentum values)
logging.info("loading Detectron weights %s", args.load_detectron)
load_detectron_weight(maskRCNN, args.load_detectron)
lr = optimizer.param_groups[0][
'lr'] # lr of non-bias parameters, for commmand line outputs.
maskRCNN = mynn.DataParallel(maskRCNN,
cpu_keywords=['im_info', 'roidb'],
minibatch=True)
### Training Setups ###
args.run_name = misc_utils.get_run_name() + '_step'
output_dir = misc_utils.get_output_dir(args, args.run_name)
args.cfg_filename = os.path.basename(args.cfg_file)
if not args.no_save:
if not os.path.exists(output_dir):
os.makedirs(output_dir)
blob = {'cfg': yaml.dump(cfg), 'args': args}
with open(os.path.join(output_dir, 'config_and_args.pkl'), 'wb') as f:
pickle.dump(blob, f, pickle.HIGHEST_PROTOCOL)
if args.use_tfboard:
from tensorboardX import SummaryWriter
# Set the Tensorboard logger
tblogger = SummaryWriter(output_dir)
### Training Loop ###
maskRCNN.train()
CHECKPOINT_PERIOD = int(cfg.TRAIN.SNAPSHOT_ITERS / cfg.NUM_GPUS)
# Set index for decay steps
decay_steps_ind = None
for i in range(1, len(cfg.SOLVER.STEPS)):
if cfg.SOLVER.STEPS[i] >= args.start_step:
decay_steps_ind = i
break
if decay_steps_ind is None:
decay_steps_ind = len(cfg.SOLVER.STEPS)
training_stats = TrainingStats(
args, args.disp_interval,
tblogger if args.use_tfboard and not args.no_save else None)
try:
logger.info('Training starts !')
step = args.start_step
for step in range(args.start_step, cfg.SOLVER.MAX_ITER):
# Warm up
if step < cfg.SOLVER.WARM_UP_ITERS:
method = cfg.SOLVER.WARM_UP_METHOD
if method == 'constant':
warmup_factor = cfg.SOLVER.WARM_UP_FACTOR
elif method == 'linear':
alpha = step / cfg.SOLVER.WARM_UP_ITERS
warmup_factor = cfg.SOLVER.WARM_UP_FACTOR * (1 -
alpha) + alpha
# warmup_factor_trans = cfg.SOLVER.WARM_UP_FACTOR_TRANS * (1 - alpha) + alpha
# warmup_factor_trans *= cfg.TRANS_HEAD.LOSS_BETA
warmup_factor_trans = 1.0
else:
raise KeyError(
'Unknown SOLVER.WARM_UP_METHOD: {}'.format(method))
lr_new = cfg.SOLVER.BASE_LR * warmup_factor
net_utils.update_learning_rate(optimizer, lr, lr_new)
lr = optimizer.param_groups[0]['lr']
assert lr == lr_new
elif step == cfg.SOLVER.WARM_UP_ITERS:
net_utils.update_learning_rate(optimizer, lr,
cfg.SOLVER.BASE_LR)
lr = optimizer.param_groups[0]['lr']
assert lr == cfg.SOLVER.BASE_LR
# Learning rate decay
if decay_steps_ind < len(cfg.SOLVER.STEPS) and \
step == cfg.SOLVER.STEPS[decay_steps_ind]:
logger.info('Decay the learning on step %d', step)
lr_new = lr * cfg.SOLVER.GAMMA
net_utils.update_learning_rate(optimizer, lr, lr_new)
lr = optimizer.param_groups[0]['lr']
assert lr == lr_new
decay_steps_ind += 1
training_stats.IterTic()
optimizer.zero_grad()
for inner_iter in range(args.iter_size):
try:
input_data = next(dataiterator)
except StopIteration:
dataiterator = iter(dataloader)
input_data = next(dataiterator)
for key in input_data:
if key != 'roidb': # roidb is a list of ndarrays with inconsistent length
input_data[key] = list(map(Variable, input_data[key]))
net_outputs = maskRCNN(**input_data)
net_outputs['losses'][
'loss_car_cls'] *= cfg.CAR_CLS.CAR_CLS_LOSS_BETA
net_outputs['losses']['loss_rot'] *= cfg.CAR_CLS.ROT_LOSS_BETA
if cfg.MODEL.TRANS_HEAD_ON:
net_outputs['losses'][
'loss_trans'] *= cfg.TRANS_HEAD.TRANS_LOSS_BETA
training_stats.UpdateIterStats_car_3d(net_outputs)
# start training
# loss_car_cls: 2.233790, loss_rot: 0.296853, loss_trans: ~100
loss = net_outputs['losses']['loss_car_cls'] + net_outputs[
'losses']['loss_rot']
if cfg.MODEL.TRANS_HEAD_ON:
loss += net_outputs['losses']['loss_trans']
if cfg.MODEL.LOSS_3D_2D_ON:
loss += net_outputs['losses']['UV_projection_loss']
if not cfg.TRAIN.FREEZE_CONV_BODY and not cfg.TRAIN.FREEZE_RPN and not cfg.TRAIN.FREEZE_FPN:
loss += net_outputs['total_loss_conv']
loss.backward()
optimizer.step()
training_stats.IterToc()
training_stats.LogIterStats(step, lr, warmup_factor_trans)
if (step + 1) % CHECKPOINT_PERIOD == 0:
save_ckpt(output_dir, args, step, train_size, maskRCNN,
optimizer)
# ---- Training ends ----
# Save last checkpoint
save_ckpt(output_dir, args, step, train_size, maskRCNN, optimizer)
except (RuntimeError, KeyboardInterrupt):
del dataiterator
logger.info('Save ckpt on exception ...')
save_ckpt(output_dir, args, step, train_size, maskRCNN, optimizer)
logger.info('Save ckpt done.')
stack_trace = traceback.format_exc()
print(stack_trace)
finally:
if args.use_tfboard and not args.no_save:
tblogger.close()
import _init_paths # pylint: disable=unused-import
import torch
from datasets.roidb import combined_roidb_for_training
from roi_data.loader import RoiDataLoader, MinibatchSampler, BatchSampler, collate_minibatch
from core.config import cfg, cfg_from_file, cfg_from_list, assert_and_infer_cfg
if __name__ == '__main__':
datasets = ('vcoco_trainval', )
batch_size = 2
num_classes = 81
cfg.RPN.RPN_ON = True
# assert_and_infer_cfg()
roidb, ratio_list, ratio_index = combined_roidb_for_training(datasets, ())
# print(roidb[0])
print(roidb[0].keys())
'''
batchSampler = BatchSampler(
sampler=MinibatchSampler(ratio_list, ratio_index),
batch_size=batch_size,
drop_last=True
)
dataset = RoiDataLoader(
roidb,
num_classes,
training=True
)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_sampler=batchSampler,
num_workers=1,
def add_model_inputs(model):
logger.info('Loading dataset: {}'.format(cfg.TRAIN.DATASET))
roidb = combined_roidb_for_training(
cfg.TRAIN.DATASET, cfg.TRAIN.PROPOSAL_FILE)
logger.info('{:d} roidb entries'.format(len(roidb)))
model_builder.add_inputs(model, roidb=roidb)
def main():
"""Main function"""
args = parse_args()
print('Called with args:')
print(args)
if not torch.cuda.is_available():
sys.exit("Need a CUDA device to run the code.")
if args.cuda or cfg.NUM_GPUS > 0:
cfg.CUDA = True
else:
raise ValueError("Need Cuda device to run !")
if args.dataset == "coco2017":
cfg.TRAIN.DATASETS = ('coco_2017_train', )
cfg.MODEL.NUM_CLASSES = 81
elif args.dataset == "coco2014":
cfg.TRAIN.DATASETS = ('coco_2014_train', )
cfg.MODEL.NUM_CLASSES = 81
elif args.dataset == 'vcoco_trainval':
cfg.TRAIN.DATASETS = ('vcoco_trainval', )
cfg.MODEL.NUM_CLASSES = 81
elif args.dataset == 'vcoco_train':
cfg.TRAIN.DATASETS = ('vcoco_train', )
cfg.MODEL.NUM_CLASSES = 81
elif args.dataset == 'vcoco_val':
cfg.TRAIN.DATASETS = ('vcoco_val', )
cfg.MODEL.NUM_CLASSES = 81
elif args.dataset == 'keypoints_coco2014':
cfg.TRAIN.DATASETS = ('keypoints_coco_2014_train', )
cfg.MODEL.NUM_CLASSES = 2
elif args.dataset == "keypoints_coco2017":
cfg.TRAIN.DATASETS = ('keypoints_coco_2017_train', )
cfg.MODEL.NUM_CLASSES = 2
else:
raise ValueError("Unexpected args.dataset: {}".format(args.dataset))
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
if args.vcoco_kp_on:
cfg.VCOCO.KEYPOINTS_ON = True
cfg.NETWORK_NAME = args.net_name # network name
print('Network name:', args.net_name)
cfg.MODEL.CONV_BODY = args.conv_body # backbone network name
print('Conv_body name:', args.conv_body)
cfg.TRAIN.FG_THRESH = args.fg_thresh
print('Train fg thresh:', args.fg_thresh)
cfg.RESNETS.FREEZE_AT = args.freeze_at
print('Freeze at: ', args.freeze_at)
cfg.VCOCO.MLP_HEAD_DIM = args.mlp_head_dim
print('MLP head dim: ', args.mlp_head_dim)
cfg.SOLVER.MAX_ITER = args.max_iter
print('MAX iter: ', args.max_iter)
cfg.TRAIN.SNAPSHOT_ITERS = args.snapshot
print('Snapshot Iters: ', args.snapshot)
if args.solver_steps is not None:
cfg.SOLVER.STEPS = args.solver_steps
print('Solver_steps: ', cfg.SOLVER.STEPS)
cfg.VCOCO.TRIPLETS_NUM_PER_IM = args.triplets_num_per_im
print('triplets_num_per_im: ', cfg.VCOCO.TRIPLETS_NUM_PER_IM)
cfg.VCOCO.HEATMAP_KERNEL_SIZE = args.heatmap_kernel_size
print('heatmap_kernel_size: ', cfg.VCOCO.HEATMAP_KERNEL_SIZE)
cfg.VCOCO.PART_CROP_SIZE = args.part_crop_size
print('part_crop_size: ', cfg.VCOCO.PART_CROP_SIZE)
print('use use_kps17 for part Align: ', args.use_kps17)
if args.use_kps17:
cfg.VCOCO.USE_KPS17 = True
else:
cfg.VCOCO.USE_KPS17 = False
print('MULTILEVEL_ROIS: ', cfg.FPN.MULTILEVEL_ROIS)
if args.vcoco_use_spatial:
cfg.VCOCO.USE_SPATIAL = True
if args.vcoco_use_union_feat:
cfg.VCOCO.USE_UNION_FEAT = True
if args.use_precomp_box:
cfg.VCOCO.USE_PRECOMP_BOX = True
cfg.DEBUG_TEST_WITH_GT = True
if args.lr is not None:
cfg.SOLVER.BASE_LR = args.lr
### Adaptively adjust some configs ###
original_batch_size = cfg.NUM_GPUS * cfg.TRAIN.IMS_PER_BATCH # 16
original_ims_per_batch = cfg.TRAIN.IMS_PER_BATCH
original_num_gpus = cfg.NUM_GPUS
if args.batch_size is None:
args.batch_size = original_batch_size
cfg.NUM_GPUS = torch.cuda.device_count()
assert (args.batch_size % cfg.NUM_GPUS) == 0, \
'batch_size: %d, NUM_GPUS: %d' % (args.batch_size, cfg.NUM_GPUS)
cfg.TRAIN.IMS_PER_BATCH = args.batch_size // cfg.NUM_GPUS
effective_batch_size = args.iter_size * args.batch_size
print('effective_batch_size = batch_size * iter_size = %d * %d' %
(args.batch_size, args.iter_size))
print('Adaptive config changes:')
print(' effective_batch_size: %d --> %d' %
(original_batch_size, effective_batch_size))
print(' NUM_GPUS: %d --> %d' %
(original_num_gpus, cfg.NUM_GPUS))
print(' IMS_PER_BATCH: %d --> %d' %
(original_ims_per_batch, cfg.TRAIN.IMS_PER_BATCH))
print(' FG_THRESH: ', cfg.TRAIN.FG_THRESH)
### Adjust learning based on batch size change linearly
# For iter_size > 1, gradients are `accumulated`, so lr is scaled based
# on batch_size instead of effective_batch_size
old_base_lr = cfg.SOLVER.BASE_LR
cfg.SOLVER.BASE_LR *= args.batch_size / original_batch_size
print('Adjust BASE_LR linearly according to batch_size change:\n'
' BASE_LR: {} --> {}'.format(old_base_lr, cfg.SOLVER.BASE_LR))
### Adjust solver steps
step_scale = original_batch_size / effective_batch_size
old_solver_steps = cfg.SOLVER.STEPS
old_max_iter = cfg.SOLVER.MAX_ITER
cfg.SOLVER.STEPS = list(
map(lambda x: int(x * step_scale + 0.5), cfg.SOLVER.STEPS))
cfg.SOLVER.MAX_ITER = int(cfg.SOLVER.MAX_ITER * step_scale + 0.5)
cfg.SOLVER.VAL_ITER = int(cfg.SOLVER.VAL_ITER * step_scale + 0.5)
cfg.TRAIN.SNAPSHOT_ITERS = int(cfg.TRAIN.SNAPSHOT_ITERS * step_scale + 0.5)
print(
'Adjust SOLVER.STEPS and SOLVER.MAX_ITER linearly based on effective_batch_size change:\n'
' SOLVER.STEPS: {} --> {}\n'
' SOLVER.MAX_ITER: {} --> {}'.format(old_solver_steps,
cfg.SOLVER.STEPS, old_max_iter,
cfg.SOLVER.MAX_ITER))
# Scale FPN rpn_proposals collect size (post_nms_topN) in `collect` function
# of `collect_and_distribute_fpn_rpn_proposals.py`
#
# post_nms_topN = int(cfg[cfg_key].RPN_POST_NMS_TOP_N * cfg.FPN.RPN_COLLECT_SCALE + 0.5)
if cfg.FPN.FPN_ON and cfg.MODEL.FASTER_RCNN:
cfg.FPN.RPN_COLLECT_SCALE = cfg.TRAIN.IMS_PER_BATCH / original_ims_per_batch
print(
'Scale FPN rpn_proposals collect size directly propotional to the change of IMS_PER_BATCH:\n'
' cfg.FPN.RPN_COLLECT_SCALE: {}'.format(
cfg.FPN.RPN_COLLECT_SCALE))
if args.num_workers is not None:
cfg.DATA_LOADER.NUM_THREADS = args.num_workers
print('Number of data loading threads: %d' % cfg.DATA_LOADER.NUM_THREADS)
# ipdb.set_trace()
### Overwrite some solver settings from command line arguments
if args.optimizer is not None:
cfg.SOLVER.TYPE = args.optimizer
if args.lr_decay_gamma is not None:
cfg.SOLVER.GAMMA = args.lr_decay_gamma
assert_and_infer_cfg()
timers = defaultdict(Timer)
### Dataset ###
timers['roidb'].tic()
roidb, ratio_list, ratio_index = combined_roidb_for_training(
cfg.TRAIN.DATASETS, cfg.TRAIN.PROPOSAL_FILES)
timers['roidb'].toc()
roidb_size = len(roidb)
logger.info('{:d} roidb entries'.format(roidb_size))
logger.info('Takes %.2f sec(s) to construct roidb',
timers['roidb'].average_time)
# Effective training sample size for one epoch
train_size = roidb_size // args.batch_size * args.batch_size
# ToDo: shuffle?
batchSampler = BatchSampler(sampler=MinibatchSampler(
ratio_list, ratio_index),
batch_size=args.batch_size,
drop_last=True)
dataset = RoiDataLoader(roidb, cfg.MODEL.NUM_CLASSES, training=True)
dataloader = torch.utils.data.DataLoader(
dataset,
batch_sampler=batchSampler,
num_workers=cfg.DATA_LOADER.NUM_THREADS,
collate_fn=collate_minibatch)
# dataiterator = iter(dataloader)
### Model ###
from modeling.model_builder import Generalized_RCNN
maskRCNN = Generalized_RCNN()
if cfg.CUDA:
maskRCNN.cuda()
### Optimizer ###
bias_hoi_params = []
bias_hoi_param_names = []
bias_faster_params = []
bias_faster_param_names = []
nobias_hoi_params = []
nobias_hoi_param_names = []
nobias_faster_params = []
nobias_faster_param_names = []
# bias_params = []
# bias_param_names = []
# nonbias_params = []
# nonbias_param_names = []
#base_model = torch.load('Outputs/baseline/baseline_512_32_nogt_1o3/ckpt/model_step47999.pth')
nograd_param_names = []
for key, value in maskRCNN.named_parameters():
#if key in base_model['model'].keys():
# value.requires_grad = False
#print('the key xxx:', key)
# Fix RPN module same as the paper
# ToDo: or key.startswith('Box')
# if 'affinity' not in key:
# value.requires_grad = False
print(key, value.size(), value.requires_grad)
if value.requires_grad:
if 'bias' in key:
if 'HOI_Head' in key:
bias_hoi_params.append(value)
bias_hoi_param_names.append(key)
else:
bias_faster_params.append(value)
bias_faster_param_names.append(key)
else:
if 'HOI_Head' in key:
nobias_hoi_params.append(value)
nobias_hoi_param_names.append(key)
else:
nobias_faster_params.append(value)
nobias_faster_param_names.append(key)
else:
nograd_param_names.append(key)
#del base_model
#ipdb.set_trace()
# Learning rate of 0 is a dummy value to be set properly at the start of training
params = [
{
'params': nobias_hoi_params,
'lr': 0,
'weight_decay': cfg.SOLVER.WEIGHT_DECAY
},
{
'params': nobias_faster_params,
'lr': 0 * cfg.SOLVER.FASTER_RCNN_WEIGHT,
'weight_decay': cfg.SOLVER.WEIGHT_DECAY
},
{
'params':
bias_hoi_params,
'lr':
0 * (cfg.SOLVER.BIAS_DOUBLE_LR + 1),
'weight_decay':
cfg.SOLVER.WEIGHT_DECAY if cfg.SOLVER.BIAS_WEIGHT_DECAY else 0
},
{
'params':
bias_faster_params,
'lr':
0 * (cfg.SOLVER.BIAS_DOUBLE_LR + 1) *
cfg.SOLVER.FASTER_RCNN_WEIGHT,
'weight_decay':
cfg.SOLVER.WEIGHT_DECAY if cfg.SOLVER.BIAS_WEIGHT_DECAY else 0
},
]
if cfg.SOLVER.TYPE == "SGD":
optimizer = torch.optim.SGD(params, momentum=cfg.SOLVER.MOMENTUM)
elif cfg.SOLVER.TYPE == "Adam":
optimizer = torch.optim.Adam(params)
### Load checkpoint
if args.load_ckpt:
load_name = args.load_ckpt
logging.info("loading checkpoint %s", load_name)
checkpoint = torch.load(load_name,
map_location=lambda storage, loc: storage)
if args.krcnn_from_faster:
net_utils.load_krcnn_from_faster(maskRCNN, checkpoint['model'])
else:
net_utils.load_ckpt(maskRCNN, checkpoint['model'])
print('Original model loaded....')
if args.resume:
print('Resume, loaded step\n\n\n: ', checkpoint['step'])
args.start_step = checkpoint['step'] + 1
if 'train_size' in checkpoint: # For backward compatibility
if checkpoint['train_size'] != train_size:
print(
'train_size value: %d different from the one in checkpoint: %d'
% (train_size, checkpoint['train_size']))
# reorder the params in optimizer checkpoint's params_groups if needed
# misc_utils.ensure_optimizer_ckpt_params_order(param_names, checkpoint)
# There is a bug in optimizer.load_state_dict on Pytorch 0.3.1.
# However it's fixed on master.
optimizer.load_state_dict(checkpoint['optimizer'])
# misc_utils.load_optimizer_state_dict(optimizer, checkpoint['optimizer'])
del checkpoint
torch.cuda.empty_cache()
if args.load_detectron: #TODO resume for detectron weights (load sgd momentum values)
logging.info("loading Detectron weights %s", args.load_detectron)
load_detectron_weight(maskRCNN, args.load_detectron)
lr = optimizer.param_groups[0][
'lr'] # lr of non-bias parameters, for commmand line outputs.
maskRCNN = mynn.DataParallel(maskRCNN,
cpu_keywords=['im_info', 'roidb'],
minibatch=True)
### Training Setups ###
args.run_name = misc_utils.get_run_name() + '_step'
#output_dir = misc_utils.get_output_dir(args, args.run_name)
output_dir = os.path.join('Outputs', args.expDir, args.expID)
os.makedirs(output_dir, exist_ok=True)
args.cfg_filename = os.path.basename(args.cfg_file)
tblogger = None
if not args.no_save:
if not os.path.exists(output_dir):
os.makedirs(output_dir)
blob = {'cfg': yaml.dump(cfg), 'args': args}
with open(os.path.join(output_dir, 'config_and_args.pkl'), 'wb') as f:
pickle.dump(blob, f, pickle.HIGHEST_PROTOCOL)
if args.use_tfboard:
from tensorboardX import SummaryWriter
# Set the Tensorboard logger
tblogger = SummaryWriter(output_dir)
### Training Loop ###
train_val(maskRCNN, args, optimizer, lr, dataloader, train_size,
output_dir, tblogger)
